(Submitted on December 18, 2018; Revised on January 12, 2019; Accepted on February 16, 2019)

Abstract:

Vessel trajectories implied in AIS data are crucial to obtain a good understanding of the maritime traffic situation for shipping safety. Starting from raw AIS data, a trajectory database is created for vessels within surveillance area after parsing, noise reduction, and DBSCAN clustering. With mmsi as the key index, the trajectory for each vessel is extracted ordering by timestamp. To remove the time interval difference between points in trajectories, interpolation and cleaning are carried out on each vessel trajectory to get trajectories with equal time intervals. Through implied motion pattern computation between adjacent points in each trajectory, maneuvering actions can be identified. Then, sailing segments with continuous same maneuvering actions are merged. With sailing segments partition results, critical points are extracted for already known different collision avoidance situations. Trajectory similarity computation for different vessels are computed with our new multi-scale and multi-resolution trajectory matching method. Experiments for the recognition of collision avoidance situations show that the adoption of the matching algorithm with multi-scale and multi-resolution trajectories for different vessel pairs to complete collision avoidance situations analysis is effective and achieves good performance.